Resumen

GONZALEZ-ABRIL, LUIS; GAVILAN, JOSE M.  y  VELASCO MORENTE, FRANCISCO. Three Similarity Measures between One-Dimensional DataSets. Rev.Colomb.Estad. [online]. 2014, vol.37, n.1, pp.79-94. ISSN 0120-1751.  https://doi.org/10.15446/rce.v37n1.44359.

Based on an interval distance, three functions are given in order to quantify similarities between one-dimensional data sets by using first-order statistics. The Glass Identification Database is used to illustrate how to analyse a data set prior to its classification and/or to exclude dimensions. Furthermore, a non-parametric hypothesis test is designed to show how these similarity measures, based on random samples from two populations, can be used to decide whether these populations are identical. Two comparative analyses are also carried out with a parametric test and a non-parametric test. This new non-parametric test performs reasonably well in comparison with classic tests.

Palabras clave : Data mining; Interval distance; Kernel methods; Non-parametric tests.

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